test_working_newhb.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
#include <Pds/Sp>
 
 
#include "extra_newhb.h"
 
// ./test/test_working_newhb
int main(void)
{
    //Pds::Ra::Randomize();
    Pds::IterationConf Conf;
    
    Pds::Octave::DAspect=true;
    Pds::Octave::YLimits=true;
    
    // Generating data
    // Data 1
    //Pds::Matrix X(Pds::Ra::TextFormat,"../test/hard-data-x.txt");
    //Pds::Vector Y(Pds::Ra::TextFormat,"../test/hard-data-y.txt");
    //X=X.SubRowMatrix(X.MeanInCols());
    
    // Data 2
    unsigned int L=1000;    Pds::Matrix X;  Pds::Vector Y;
    Pds::DataSet::Classification::LoadDataBand(L,X,Y);
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","test_working_newhb.png");
    
    unsigned int N=128;
    double b=2.0;
    Pds::Vector w1=Pds::LinSpace(-b,b,N);
    Pds::Vector w2=Pds::LinSpace(-b,b,N);
    Pds::Matrix MS(N,N); 
    Pds::Matrix CE(N,N); 
    Pds::Matrix IG(N,N); 
    Pds::Matrix IS(N,N); 
    double val;
 
    Pds::Vector w({0.25,0.0,0.0});
    
    w.Set(1,-0.5); w.Set(2, 0.5);
    Pds::Octave::Plot::PointsX2DYW(X,Y,w,"testando_w.m","test_working_w0.png");
    w.Set(1, 0.5); w.Set(2,-0.5);
    Pds::Octave::Plot::PointsX2DYW(X,Y,w,"testando_w.m","test_working_w1.png");
    w.Set(1, 1.0); w.Set(2, 1.0);
    Pds::Octave::Plot::PointsX2DYW(X,Y,w,"testando_w.m","test_working_w2.png");
    w=Pds::LogisticModel::FittingLogitMeanSquare(Conf,X,Y,0.01);
    w.T().Print("w:\n");    w.Normalize();
    Pds::Octave::Plot::PointsX2DYW(X,Y,w,"testando_w.m","test_working_w3.png");
    w.T().Print("w:\n");
    
    double d=Pds::DataSet::InterDistance(X);
    double dinv=1.0/d;
    std::cout<<"  d:\t"<<d<<"\n";
    std::cout<<"1/d:\t"<<dinv<<"\n";
    
    for(unsigned int i=0;i<N;i++)
    for(unsigned int j=0;j<N;j++)
    {
        w.Set(1,w1[i]);
        w.Set(2,w2[j]);
        
        val=Pds::LogisticModel::CostInformationGain( w,X,Y);
        IG.SetRaw(i,j,val);
        
        val=Pds::LogisticModel::CostMeanSquare( dinv*w/w.Norm(),X,Y);
        MS.SetRaw(i,j,val);
        
        val=Pds::LogisticModel::CostCrossEntropy( 10*dinv*w/w.Norm(),X,Y);
        CE.SetRaw(i,j,val);
        
        val=Pds::LogisticModel::CostXqEntropy( w,X,Y,q_factor_val); //w*(100/w.Norm())
        IS.SetRaw(i,j,val);
    }
 
    // No treinar bias
 
    Pds::Matrix W1,W2;
    Pds::Meshgrid (w1,w2,W1,W2);
    
    Pds::Octave::DAspect=false;
    Pds::Octave::Plot::SurfC(W1,W2,MS,"testando_ms.m","ms_matrix.png"); 
    Pds::Octave::Plot::SurfC(W1,W2,CE,"testando_ce.m","ce_matrix.png"); 
    Pds::Octave::Plot::SurfC(W1,W2,(1-IG),"testando_ig.m","ig_matrix.png"); 
    Pds::Octave::Plot::SurfC(W1,W2,IS,"testando_is.m","is_matrix.png"); 
 
    return 0;
}